Data Mining – Unsupervised Learning

Data Mining - Unsupervised Learning

Created by: AISPRY TUTOR
AISPRY Tutor is a branch of learning platform with360DigitMG

What Will I Learn?

• In Clustering or Segmentation, we reduce the number of rows. We have Hierarchical Clustering, Non-Hierarchical, Density-Based Clustering, Grid-based Clustering
• In Dimension Reduction, we reduce the number of columns. Linear Patterns are handled by Linear Discriminant Analysis, Non-negative Matrix Factorization.
• There is Collaborative Filtering in Recommendation System. Traditional Collaborative Filtering, Search-based Method, and Item-Item Collaborative Filtering.
• In Unsupervised Learning has 6 divisions which include Clustering, Dimension Reduction, Association Rules, Recommendation Syst

Requirements

• Basic Knowledge of Data Mining is typically expected that students have a foundational understanding of data mining concepts and techniques.
• Analytical Thinking and Problem-Solving Skills
• No prior coding knowledge is necessary. You will pick up all the information you require. Basic Knowledge of Mathematics and Statistics.
• A solid foundation in linear algebra and calculus can be helpful in understanding the mathematical underpinnings of unsupervised learning algorithms.

Target audience

• IT professionals, including software developers and database administrators, can gain valuable skills by taking a Data Mining course.
• Researchers who want to explore and analyze large datasets, discover patterns, and generate insights can benefit from a Data Mining course to learn appropriate methodologies and techniques.
• Professionals who want to enhance their skills in data analysis, pattern recognition, and predictive modeling can enroll in a Data Mining course to learn new techniques and stay updated with industry trends.
• Students pursuing degrees in these areas can benefit from a Data Mining course to develop skills and knowledge related to analyzing and extracting insights from large datasets.